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Found 872 Skills
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
Used to discover the minimal change that a target truly accomplishes: put identity, boundaries, quality, optimization metrics, and implementations in parentheses, keep only the concise core of 'who or what goes from A to B', compress it into the minimal structural formula of interpretable variables, verify whether it is truly decontextualized through cross-domain migration, and save it as a validated Org note. USE WHEN the user says '/ljg-is', 'What is the essence of X', 'The One', 'Find the essence of the target', 'Essence definition', 'Minimum function', 'What exactly is transformed into what', or asks to distinguish qualifiers from the core. NOT FOR asking about reasons (use ljg-think), seeking motifs or causal structures (use ljg-structure), or casually saying 'essentially' in a sentence.
Verify and release Three.js browser games. Combines playtest QA, automated bot playtests, mobile/responsive checks, production builds, preview verification, static-hosting base paths, debug gating, bundle review, screenshots, visual test harness decisions, packaged canvas-pixel inspection with measured metrics, console checks, and release risk reports.
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.
Gate 1: Business requirements document - defines WHAT/WHY before HOW. Creates PRD with problem definition, user stories, success metrics.
Diagnoses Google Ads account performance issues such as conversion loss (value or volume), low lead flow/volume, and lost impression share (opportunities) due to ad rank, bids, or budgets. Use when troubleshooting sudden performance drops, analyzing campaign impression share metrics, investigating low lead flow, or searching for bidding and budget constraints. Don't use for setting up new campaigns, uploading conversion events directly, or general Google Mobile Ads SDK integration issues (use gma-android-integrate instead).
Define, track, and act on QA metrics: test coverage percentage, flakiness rate, defect escape rate, MTTR, test execution time trends, automation ROI, quality gates, and SLAs for test suites. Includes metric formulas, realistic targets by company stage, and the action to take when each metric goes red. Use when: "QA metrics," "test metrics," "quality KPIs," "test health," "flakiness rate," "defect escape rate." Not for: building the dashboard UI (Allure/Grafana) — use qa-dashboard; measuring coverage gaps and mutation score — use coverage-analysis. Related: qa-dashboard, coverage-analysis, ci-cd-integration, release-readiness, quality-postmortem.
Safe-release techniques DURING rollout: feature flags, progressive rollouts, canary analysis, guardrail metrics, production smoke tests, and synthetic users. Bridges QA and SRE practices. Use when: "feature flag testing," "canary deploy," "progressive rollout," "guardrail metrics," "dark launch," "safe rollout." Not for: scheduled probes that run continuously after release — use `synthetic-monitoring`. Not for: designing tests from prod telemetry — use `observability-driven-testing`. Related: release-readiness, synthetic-monitoring, observability-driven-testing, qa-metrics.
Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
Identify which field values correlate with bad behavior (slowness, errors, anomalies, unusual values) using phi-coefficient correlation analysis over OPAL. Works on any time-series data — metrics, structured logs, span/trace data, or any dataset where rows can be split into a 'bad' and 'good' cohort by a threshold. Use when: (1) User asks for root-cause analysis on a dataset or metric (2) User wants to know what attributes / dimensions / values are most associated with a failure mode, anomaly, or unusual cohort (3) Investigating which services, hosts, regions, namespaces, or attributes drive outliers (4) User mentions phi coefficient, correlation, or outlier detection (5) User asks 'why is X slow/failing', 'what caused the errors on X', or 'what's different about the bad cohort'.
**ANALYSIS SKILL** - Observe Aspire apps: logs, traces, metrics, resource state, telemetry export, browser telemetry, and the standalone dashboard. Routes between local Aspire CLI, AKS workload diagnostics, and deployed Azure resource health. USE FOR: aspire logs, aspire otel logs, aspire otel traces, aspire otel spans, aspire describe, aspire ps, aspire export, aspire dashboard run, --include-hidden, browser logs in dashboard, WithBrowserLogs, App Insights query, AKS pod logs, container app logs. DO NOT USE FOR: start/stop/wait (use aspire-orchestration), deploy/publish/destroy (use aspire-deployment), AppHost code edits like WithBrowserLogs() (use aspireify), Azure provisioning (use azure-prepare). INVOKES: aspire CLI, azure-diagnostics (deployed Azure), kubectl + Container Insights. FOR SINGLE OPERATIONS: Run the aspire CLI command directly for quick log or describe lookups.
**WORKFLOW SKILL** — Manage Aspire AppHost lifecycle and recover from file locks, port conflicts, and orphaned processes. WHEN: "start my Aspire app", "aspire start", "aspire stop", "aspire wait", "restart the API service", "file lock error", "MSB3491", "CS2012", "port already in use", "upgrade Aspire CLI", "aspire update --self", "proxies missing in aspire ps", "--include-hidden", "aspire integration list", "aspire integration search", "default watch", "hot reload". INVOKES: aspire CLI (start, stop, wait, ps, resource, integration, add, init, doctor, update, restore). DO NOT USE FOR: deploy / publish / destroy / pipeline steps (use aspire-deployment), logs / traces / metrics / dashboard run (use aspire-monitoring), AppHost code edits or resource wiring (use aspireify). FOR SINGLE OPERATIONS: Run the aspire CLI command directly.